Transformer differential protection using wavelet transform

Özgönenel O., Karagöl S.

ELECTRIC POWER SYSTEMS RESEARCH, vol.114, pp.60-67, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 114
  • Publication Date: 2014
  • Doi Number: 10.1016/j.epsr.2014.04.008
  • Journal Name: ELECTRIC POWER SYSTEMS RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.60-67
  • Keywords: Entropy approach, Minimum description length criterion, Inrush current, Internal fault, Power transformer, Wavelet packet analysis, ARTIFICIAL NEURAL-NETWORK, POWER TRANSFORMERS, IMPROVED OPERATION, INTERNAL FAULTS, CLASSIFICATION
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

This paper will propose a cascade of minimum description length criterion with entropy approach along with artificial neural network (ANN) as an optimal feature extraction and selection tool for a wavelet packet transform based transformer differential protection. The proposed protection method provides a reliable and computationally efficient tool for distinguishing between internal faults and inrush currents. The role of minimum description length criterion with entropy approach has been found to improve the efficiency of ANN with the dimensionality reduction of the feature vector. This reduction plays a major role in preventing the redundancy effect that can occur when using several features in an intelligent based monitoring system. (C) 2014 Elsevier B.V. All rights reserved.